Clinical decision support systems

Abstract

Clinical decision support (CDS) systems link patient data with an electronic knowledge base in order to improve decision-making and computerised physician order entry (CPOE) is a requirement to set up electronic CDS. The medical informatics literature suggests categorising CDS tools into medication dosing support, order facilitators, point-of-care alerts and reminders, relevant information display, expert systems and workflow support. To date, CDS has particularly been recognised for improving processes. CDS successfully fostered prevention of deep-vein thrombosis, improved adherence to guidelines, increased the use of vaccinations, and decreased the rate of serious medication errors. However, CDS may introduce errors, and therefore the term "e-iatrogenesis" has been proposed to address unintended consequences. At least two studies reported severe treatment delays due to CPOE and CDS. In addition, the phenomenon of "alert fatigue" - arising from a high number of CDS alerts of low clinical significance - may facilitate overriding of potentially critical notifications. The implementation of CDS needs to be carefully planned, CDS interventions should be thoroughly examined in pilot wards only, and then stepwise introduced. A crucial feature of CPOE in combination with CDS is speed, since time consumption has been found to be a major factor determining failure. In the near future, the specificity of alerts will be improved, notifications will be prioritised and offer detailed advice, customisation of CDS will play an increasing role, and finally, CDS is heading for patient-centred decision support. The most important research question remains whether CDS is able to improve patient outcomes beyond processes.

Which QT Correction Formulae to Use for QT Monitoring?

“…This implies that a hospital‐wide automated algorithm, which uses QTcB for assessment of a possible dangerous QT prolongation, would generate double the amount of alerts compared to an algorithm using the optimal QT correction formula. This could lead to alert fatigue and avoiding clinically indicated first‐choice drugs because of potential QT‐prolonging effects . Hence, using an optimal QT correction formula could reduce the workload and improve patient safety.…”

Utility of Electronic Medical Record Alerts to Prevent Drug Nephrotoxicity

“…Several studies have shown the potential efficacy of alerts in the hospital setting (14)(15)(16); however, extensive reviews of the clinical decision support literature have consistently described specific elements that increase provider adherence and thus, the likelihood of alert success (17)(18)(19)(20). These factors include the speed of the information system, timing of the alert (real time and at the point of care), minimal disruption of and integration into provider workflow, simplicity and clarity of the message, and provision of references and sufficient information within the alert.…”

“…Alert fatigue is more likely when endogenous recognition and exogenous recognition are in conflict (i.e., an alert occurs for a condition of which a provider is already aware). Additionally, alert fatigue can occur due simply to the proliferation of alerts, even if they are informative (20,29,30). This is well documented in the intensive care unit, where frequent chimes, buzzers, and beeping fade quickly into background obscurity.…”

Therapy Decision Support Based on Recommender System Methods

“…Generally, CDSS are designated to assist physicians or other health professionals during clinical decision-making. CDSS are demanded to be integrated into the clinical workflow and to provide decision support at time and location of care [1]. Data-driven CDSS, in particular, make use of data-mining and machine-learning techniques to extract and combine relevant information from patient data, in order to provide assistance for diagnosis and treatment decisions or even to be used in clinical quality control based on large-scale data [1].…”